ALBERT

Note: In kyrillischer Schrift

Note: CONTENTS Abstract Preface Conversion factors: U.S. customary to metric units of measurement Introduction Background Previous investigations Analytical procedures Results and discussion Shoreline changes: High-water line Shoreline changes: Cliff recession Volumetric changes Nodal zone location and direction of littoral transport Error evaluation Summary and conclusions Future research Selected bibliography Appendix A: Maps of shoreline with overlays for 1938, 1952, 1971 and 1974 Appendix B: Supplementary data for estimating shoreline change

Note: CONTENTS Abstract Preface Nomenclature Introduction Background Experimental design Similitude considerations The model Model operation Results Velocity Fence height Porosity Model correlation Summary and conclusions Recommeqdations for future work Literature cited Appendix A

Note: CONTENTS Abstract Preface Introduction and statement of problem Review of relevant analyses and tests Determination of the largest ice force on an isolated structure Preliminary remarks The buckling analysis of a floating wedge Proposed method to determine Literature cited

Note: CONTENTS Abstract Preface Nomenclature Introduction 1. The problem 2. Abstract of the result Part I. Fundamental solutions 3. Fuchsian type solutions 4. Contour integral solution 5. Integration of the integral solution 6. Fundamental solutions for α = 1 7. Fundamental solutions for α = 0 8. Eigenvalues for α = 0 9. Fundamental solutions for α 〉 1 Part II. Asymptotic expansions 10. Asymptotic expansion for 0 〈 α ⩽1 11. Asymptotic expansion for 1 ≦ α ≦ 2 12. Asymptotic expansion for 2 ≦ α ≦ ∞ Part III. Eigenvalues 13. Range of eigenvalues 14. Eigenvalues for the free-edge condition 15. Eigenvalues for the clamped-edge and simple-edge conditions 16. Deflection Acknowledgement Literature cited Appendix A. Analytical continuation at the singular point Appendix B. Tensorial transformations Appendix C. Comparison of the semi-infinite plate buckling with the asymptotic buckling

Note: CONTENTS Abstract Preface Conversion factors: U.S.customary to metric units of measurement Introduction Freeze-thaw system Construction materials Controlled environment test section Outdoor sections Temperature monitoring instrumentation Repetitive plate bearing test apparatus Test results and analyses Conclusions Literature cited

Note: CONTENTS Abstract Preface Summary Introduction Background Objective and procedure Theory of plane surface waves Waveguide design and characteristics Physical apparatus Frequency characteristics Spatial distribution of Ey above the guide Guide wave length Surface wave interaction with a slab discontinuity Experiments with a trough Discussion and conclusions Literature cited

Note: CONTENTS Abstract Preface Introduction The problem The solution Method of numerical integration Ramp/steady loading Curve fitting to time lapse deflections Asymptotic deflection Deflection profiles Acknowledgement Literature cited Appendix I. Analytical background Appendix II. Computer programs, ramp time profiles and steady time profiles

Note: CONTENTS Abstract Preface Conversion factors: U.S. customary to metric (SI) units of measurement Introduction Theory Solution by superposition Solution by Fourier integral Solution by Fourier series Application and results Literature cited Appendix A: Bearing capacity of river ice for military vehicles Appendix B: Vehicle specifications Appendix C: Fortran II computer program

Note: CONTENTS Abstract Preface Nomenclature Introduction Derivation of the power reflection coefficient-depth variation Possible sources of dielectric constant variation with depth in ice sheets Ice density variations Effect of macroscopic impurity layers Effect of microscopic impurtty layers Changes in inclusion geometry Effect of crystal orientation changes Physical properties of the Cape Folger core Density-depth profile Air bubble-geometry depth profile Crystal orientation-depth profile Radio-echo sounding data Results and conclusions Literature cited

Note: CONTENTS Abstract Preface Introduction Sources and properties of tabular icebergs Sources Characteristics of ice shelves near the ice front Characteristics of tabular icebergs Towing Geophysical and engineering considerations Tug characteristics Handling and processing Cutting and boring with thermal devices Penetration with electrothermal devices Electrothermal cutting Making vertical cuts by pre-split blasting Primary fragmentation by blasting Primary fragmentation by mechanical sawing Comminuting ice with machines Slurry pipelines Conclusion Literature cited

Note: CONTENTS Abstract Preface Introduction Test procedure Test program Test samples Number of test for determining strength index Effect of temperature Effect of sample length Effect of load point size Tests on snow Discussion Recommendations Literature cited

Note: Contents 1 Introduction 1.1 Purpose 1.2 Some terminology 1.3 Electrical analogues 1.4 State determined systems 1.5 Simulation languages 2 Exponential growth 2.1 Analytical and numerical integration 2.2 Simulation 2.3 Time constant and time interval of integration 2.4 Integration method and time interval 3 The growth of yeast 3.1 Description of the system 3.2 Relational diagrams 3.3 Simulation 3.4 Logistic growth 4 Interference of plants 4.1 Replacement series 4.2 Density of sowing 4.3 Simulation of plant interference 4.4 Further modelling aspects 4.4.1 The INDEX and MACRO feature 4.4.2 The INITIAL and DYNAMIC section 5 Growth and competition of Paramecium 5.1 Description of the system 5.2 A simulation program 5.3 Preliminary estimation of parameters 5.4 Final determination of parameters 5.5 Stochastic aspects 5.6 The programming of probability functions 6 Modelling of development, dispersion and diffusion 6.1 Introduction 6.2 Physiological age and development stage 6.3 Demographic models 6.3.1 Age-classes 6.3.2 Errors of approximation 6.3.3 The matrix method 6.4 Germination models 6.4.1 Boxcar train without dispersion 6.4.2 Boxcar train with constant relative dispersion 6.4.3 Boxcar train with controlled dispersion 6.4.4 Refinements 6.5 The flow of heat in soils 7 Growth and development of Helminthosporium maydis 7.1 Introduction 7.2 The weather 7.3 Appearance and growth of lesions 7.4 Sporophore or stalk formation 7.5 Sporulation of green stalks 7.6 Sporulation of dried stalks 7.7 Dispersal of spores 7.8 Germination of spores and penetration of germtubes 7.9 Timing, initialization and output organization 7.10 Results and sensitivity analyses 8 Population models for fruit-tree red spider miteand predatory mites 8.1 Fruit-tree red spider mite 8.2 Predatory mite, Amblyseius potentillae 8.3 Relations between predator and prey 8.4 Preference as a competitive process 8.5 Verification 9 Solutions of the exercises References Appendix A Appendix B Index

Note: Contents Preface to the Second Edition Preface to the First Edition 1. Images, Arrays, and Matrices 1.1 Multispectral Satellite Images 1.2 Algebra of Vectors and Matrices 1.2.1 Elementary Properties 1.2.2 Square Matrices 1.2.3 Singular Matrices 1.2.4 Symmetric, Positive Definite Matrices 1.2.5 Linear Dependence and Vector Spaces 1.3 Eigenvalues and Eigenvectors 1.4 Singular Value Decomposition 1.5 Vector Derivatives 1.6 Finding Minima and Maxima 1.7 Exercises 2. Image Statistics 2.1 Random Variables 2.1.1 Discrete Random Variables 2.1.2 Continuous Random Variables 2.1.3 Normal Distribution 2.2 Random Vectors 2.3 Parameter Estimation 2.3.1 Sampling a Distribution 2.3.2 Interval Estimation 2.3.3 Provisional Means 2.4 Hypothesis Testing and Sample Distribution Functions 2.4.1 Chi-Square Distribution 2.4.2 Student-t Distribution 2.4.3 F-Distribution 2.5 Conditional Probabilities, Bayes' Theorem, and Classification 2.6 Ordinary Linear Regression 2.6.1 One Independent Variable 2.6.2 More Than One Independent Variable 2.6.3 Regularization, Duality, and the Gram Matrix 2.7 Entropy and Information 2.7.1 Kullback-Leibler Divergence 2.7.2 Mutual Information 2.8 Exercises 3. Transformations 3.1 Discrete Fourier Transform 3.2 Discrete Wavelet Transform 3.2.1 Haar Wavelets 3.2.2 Image Compression 3.2.3 Multiresolution Analysis 3.2.3.1 Dilation Equation and Refinement Coefficients 3.2.3.2 Cascade Algorithm 3.2.3.3 Mother Wavelet 3.2.3.4 Daubechies D4 Scaling Function 3.3 Principal Components 3.3.1 Primal Solution 3.3.2 Dual Solution 3.4 Minimum Noise Fraction 3.4.1 Additive Noise 3.4.2 Minimum Noise Fraction Transformation in ENVI 3.5 Spatial Correlation 3.5.1 Maximum Autocorrelation Factor 3.5.2 Noise Estimation 3.6 Exercises 4. Filters, Kernels, and Fields 4.1 Convolution Theorem 4.2 Linear Filters 4.3 Wavelets and Filter Banks 4.3.1 One-Dimensional Arrays 4.3.2 Two-Dimensional Arrays 4.4 Kernel Methods 4.4.1 Valid Kernels 4.4.2 Kernel PCA 4.5 Gibbs-Markov Random Fields 4.6 Exercises 5. Image Enhancement and Correction 5.1 Lookup Tables and Histogram Functions 5.2 Filtering and Feature Extraction 5.2.1 Edge Detection 5.2.2 Invariant Moments 5.3 Panchromatic Sharpening 5.3.1 HSV Fusion 5.3.2 Brovey Fusion 5.3.3 PCA Fusion 5.3.4 DWT Fusion 5.3.5 A Trous Fusion 5.3.6 Quality Index 5.4 Topographic Correction 5.4.1 Rotation, Scaling, and Translation 5.4.2 Imaging Transformations 5.4.3 Camera Models and RFM Approximations 5.4.4 Stereo Imaging and Digital Elevation Models 5.4.5 Slope and Aspect 5.4.6 Illumination Correction 5.5 Image-Image Registration 5.5.1 Frequency-Domain Registration 5.5.2 Feature Matching 5.5.2.1 High-Pass Filtering 5.5.2.2 Closed Contours 5.5.2.3 Chain Codes and Moments 5.5.2.4 Contour Matching 5.5.2.5 Consistency Check 5.5.2.6 Implementation in IDL 5.5.3 Resampling and Warping 5.6 Exercises 6. Supervised Classification: Part 1 6.1 Maximum a Posteriori Probability 6.2 Training Data and Separability 6.3 Maximum Likelihood Classification 6.3.1 ENVI's Maximum Likelihood Classifier 6.3.2 Modified Maximum Likelihood Classifier 6.4 Gaussian Kernel Classification 6.5 Neural Networks 6.5.1 Neural Network Classifier 6.5.2 Cost Functions 6.5.3 Backpropagation 6.5.4 Overfitting and Generalization 6.6 Support Vector Machines 6.6.1 Linearly Separable Classes 6.6.1.1 Primal Formulation 6.6.1.2 Dual Formulation 6.6.1.3 Quadratic Programming and Support Vectors 6.6.2 Overlapping Classes 6.6.3 Solution with Sequential Minimal Optimization 6.6.4 Multiclass SVMs 6.6.5 Kernel Substitution 6.6.6 Modified SVM Classifier 6.7 Exercises 7. Supervised Classification: Part 2 7.1 Postprocessing 7.1.1 Majority Filtering 7.1.2 Probabilistic Label Relaxation 7.2 Evaluation and Comparison of Classification Accuracy 7.2.1 Accuracy Assessment 7.2.2 Model Comparison 7.3 Adaptive Boosting 7.4 Hyperspectral Analysis 7.4.1 Spectral Mixture Modeling 7.4.2 Unconstrained Linear Unmixing 7.4.3 Intrinsic End-Members and Pixel Purity 7.5 Exercises 8. Unsupervised Classification 8.1 Simple Cost Functions 8.2 Algorithms That Minimize the Simple Cost Functions 8.2.1 K-Means Clustering 8.2.2 Kernel K-Means Clustering 8.2.3 Extended K-Means Clustering 8.2.4 Agglomerative Hierarchical Clustering 8.2.5 Fuzzy K-Means Clustering 8.3 Gaussian Mixture Clustering 8.3.1 Expectation Maximization 8.3.2 Simulated Annealing 8.3.3 Partition Density 8.3.4 Implementation Notes 8.4 Including Spatial Information 8.4.1 Multiresolution Clustering 8.4.2 Spatial Clustering 8.5 Benchmark 8.6 Kohonen Self-Organizing Map 8.7 Image Segmentation 8.7.1 Segmenting a Classified Image 8.7.2 Object-Based Classification 8.7.3 Mean Shift 8.8 Exercises 9. Change Detection 9.1 Algebraic Methods 9.2 Postclassification Comparison 9.3 Principal Components Analysis 9.3.1 Iterated PCA 9.3.2 Kernel PCA 9.4 Multivariate Alteration Detection 9.4.1 Canonical Correlation Analysis 9.4.2 Orthogonality Properties 9.4.3 Scale Invariance 9.4.4 Iteratively Reweighted MAD 9.4.5 Correlation with the Original Observations 9.4.6 Regularization 9.4.7 Postprocessing 9.5 Decision Thresholds and Unsupervised Classification of Changes 9.6 Radiometrie Normalization 9.7 Exercises Appendix A: Mathematical Tools A.l Cholesky Decomposition A.2 Vector and Inner Product Spaces A.3 Least Squares Procedures A.3.1 Recursive Linear Regression A.3.2 Orthogonal Linear Regression Appendix B: Efficient Neural Network Training Algorithms B.1 Hessian Matrix B.1.1 R-Operator B.1.1.1 Determination of Rv{n} B.1.1.2 Determination of Rv{δo} B.1.1.3 Determination of Rv{δh} B.1.2 Calculating the Hessian B.2 Scaled Conjugate Gradient Training B.2.1 Conjugate Directions B.2.2 Minimizing a Quadratic Function B.2.3 Algorithm B.3 Kaiman Filter Training B.3.1 Linearization B.3.2 Algorithm B.4 A Neural Network Classifier with Hybrid Training Appendix C: ENVI Extensions in IDL C.1 Installation C.2 Extensions C.2.1 Kernel Principal Components Analysis C.2.2 Discrete Wavelet Transform Fusion C.2.3 A Trous Wavelet Transform Fusion C.2.4 Quality Index C.2.5 Calculating Heights of Man-Made Structures in High-Resolution Imagery C.2.6 Illumination Correction C.2.7 Image Registration C.2.8 Maximum Likelihood Classification C.2.9 Gaussian Kernel Classification C.2.10 Neural Network Classification C.2.11 Support Vector Machine Classification C.2.12 Probabilistic Label Relaxation C.2.13 Classifier Evaluation and Comparison C.2.14 Adaptive Boosting a Neural Network Classifier C.2.15 Kernel K-Means Clustering C.2.16 Agglomerative Hierarchical Clustering C.2.17 Fuzzy K-Means Clustering C.2.18 Gaussian Mixture Clustering C.2.19 Kohonen Self-Organizing Map C.2.20 Classified Image Segmentation C.2.21 Mean Shift Segmentation C.2.22 Multivariate Alteration Detection C.2.23 Viewing Changes C.2.24 Radiometric Normalization Appendix D: Mathematical Notation References Index

Note: Zugl.: Potsdam, Univ., Diss., 2010